In this paper, we propose a novel predictive safety filter that is robust to
bounded perturbations and is combined with a learning-based control called
differentiable predictive control (DPC). The proposed method provides rigorous
guarantees of safety in the presence of bounded perturbations and implements
DPC so long as the DPC control satisfies the system constraints. The approach
also incorporates two forms of event-triggering to reduce online computation.
The approach is comprised of a robust predictive safety filter that extends
upon existing work to reject disturbances for discrete-time, time-varying
nonlinear systems with time-varying constraints. The safety filter is based on
novel concepts of robust, discrete-time barrier functions and can be used to
filter any control law. Here we use the safety filter in conjunction with DPC
as a promising policy optimization method. The approach is demonstrated on a
single-integrator, two-tank system, and building example.Comment: Submitted to Automatic